Road transportation in most mega cities, such as those with more than 5 million popular, faces traffic congestion issues caused by increasing number of vehicles and accidents. Since new road construction can be very expensive and sometimes unfeasible due to geographic, structural, or restraints, municipalities are often limited to the existing road infrastructure, and thus unable to cope with the increasing demand. As a result, drivers around the world are faced with increasing traffic congestion and longer travel time. This produces an enormous waste of natural resources and productivity, and generates additional pollution.
Efficient trip planning is difficult because road transportation is spontaneous, with many unexpected/unpredictable but frequently occurring events such as accidents, oil spills, construction, weather and many other factors, which can all contribute to unexpected/unpredictable traffic delays.
While there are existing systems that attempt to address aspects of traffic issues, those systems often focus on the so-called live traffic report and do not address the core of the problem—the lack of sensible and feasible planning.
For example, IBM provides a traffic prediction tool (http://www.ibm.com/smarterplanet/us/en/transportation_systems/nextsteps/solution/N500945X17585D04.html) that utilizes historical traffic data and real-time traffic input from a city's transport system to predict traffic flows over pre-set durations of 10, 15, 30, 45, and 60 minutes. This tool, however, is provided at the macro level for traffic controllers/operators to anticipate and better manage the flow of traffic to prevent the buildup of congestion (e.g., in pro-active signal setting and ramp metering), and does not provide functionality for the individual drivers (end users).
Existing trip planning devices such as portable automotive GPS systems or mobile devices running trip planning applications like Google Map also suffer from a number of disadvantages. For example, while existing trip planning devices and services can provide a live traffic layer on top of personalized trip planning using various sensor networks or crowd sourcing, they unnecessarily transmit a lot of irrelevant/extraneous data to the users. For example, when a user is traveling from point A to B, he/she is typically only interested in things that are relevant along the route, between the two points. Existing applications will nevertheless stream all traffic data across the entire area along the route. Transmitting more data than necessary reduces both application performance and usability. Furthermore, the so-called “live” information is actually based on data that are approximately 5-10 minutes old, and thus may no longer be accurate since, e.g., traffic can build up or dissipate quickly. Consequently, a route that is generated based on old and potentially inaccurate data (e.g. to avoid a previous congested section) may not be efficient if, e.g., the traffic congestion of a road section had already cleared up. Furthermore, these devices/applications cannot predict future congestions.